Demographics

Everyone

Demographics for all included participants.

Demographics
Summary
N Age (years) Education (years) Sex (M/F/O) EHI
621 29.27 (6) 14.31 (2.4) 336/276/9 30.33 (76.91)



Race n
White 452
Black or African American 57
Asian 54
Multiple 51
American Indian or Alaska Native 5
Native Hawaiian or Other Pacific Islander 1
Other 1


Hispanic ethnicity n
No 554
Yes 67


By handedness group

Demographics for included participants, by handedness group (EHI bins).

Handedness N Age (years) Education (years) Sex (M/F/O) EHI
Left 171 29.14 (6.27) 14.25 (2.27) 90/79/2 -80.19 (19.96)
Mixed 78 29.08 (6.08) 14.59 (2.32) 49/27/2 -5.61 (26.7)
Right 372 29.37 (5.88) 14.28 (2.47) 197/170/5 88.68 (15.52)
Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40)


Field x Level x Handedness (binned)

Within each handedness group, do we see the typical field x level interaction? That is, do participants show a relative bias for global shapes in the left visual field (LVF)?

Reaction time

Plots

Error bars show 95% CI.



Statistics

Simple mixed regression model

Reaction time is modeled as a linear effect of field and level, using data from every target-present trial with a “go” response:

lmer( rt ~ field*level*handedness + (1 | subject) )


Field by level by handedness interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value
9 809,804.09 809,885.087 −404,893.045 809,786.09 - - -
10 809,804.25 809,894.246 −404,892.125 809,784.25 1.84 1 .17


Field by level interaction (RT)
Omnibus F-test
term df sumsq meansq statistic p.value
field 1 532,813.683 532,813.683 7.665 .006
level 1 6,371,638.985 6,371,638.985 91.665 <.0001
handedness 1 3,373,026.257 3,373,026.257 48.526 <.0001
field:level 1 2,270,639.42 2,270,639.42 32.667 <.0001
field:handedness 1 967,375.584 967,375.584 13.917 .0002
level:handedness 1 12,628.623 12,628.623 0.182 .67
field:level:handedness 1 48,725.68 48,725.68 0.701 .4
Residuals 59,843 4,159,669,538.499 69,509.709 - -


Field by level by handedness interaction (RT)
Compare effect estimate to zero with emmeans()
field_consec level_consec handedness_consec estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Right - Left 10.322 7.61 Inf −4.594 25.238 1.356 .17
1 A positive number means LVF global bias is stronger in right handers (as predicted by AAH)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided


LVF Global bias by handedness bin (RT)
field_consec level_consec handedness estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Left 17.497 5.747 Inf 6.234 28.76 3.045 .002
LVF - RVF Local - Global Mixed 20.542 8.493 Inf 3.897 37.188 2.419 .02
LVF - RVF Local - Global Right 27.282 3.898 Inf 19.643 34.922 6.999 <.0001
1 A positive number means global bias (faster RT for global)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided, uncorrected


Accuracy

In progress.

Field x Level x Handedness (continuous)

Reaction time

Plots

Statistics

Model RT as a linear effect of field, level, and EHI (continuous):

rt_ehi_model <- lmer( rt ~ field*level*ehi + (1 | subject) ) 

## Use anova() on competing models to test 3-way interaction.
interaction_stats <-
  function(model_with_interaction,
           model_with_no_interaction) {
    return(anova(model_with_interaction, model_with_no_interaction))
  }

rt_model_no_interaction <- update(rt_model_ehi, . ~ . - field:level:ehi)
interaction_anova <- interaction_stats(rt_model_ehi, rt_model_no_interaction)
interaction_anova |>
  as_tibble() |>
  rename(p.value = `Pr(>Chisq)`) |> 
  format_p.value() |> 
  pretty_table() |> 
  tab_header(title = "Field by level by ehi interaction (RT)", 
             subtitle = "ANOVA: compare models with vs. without interaction term")
Field by level by ehi interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value
9 1,021,589.879 1,021,672.961 −510,785.94 1,021,571.879 - - -
10 1,021,589.309 1,021,681.622 −510,784.655 1,021,569.309 2.57 1 .11


Estimated global bias by field, for EHI of -100
contrast estimate1 SE df z.ratio p.value
(LVF Local -100) - (LVF Global -100) 31.6 4.199 Inf 7.525 <.0001
(RVF Local -100) - (RVF Global -100) 16.054 4.199 Inf 3.823 .0008
1 Estimated global bias (ms)



Estimated LVF Global Bias for EHI of -100
LVF_global_bias
15.546


Estimated global bias by field, for EHI of +100
contrast estimate1 SE df z.ratio p.value
LVF Local 100 - LVF Global 100 35.347 2.874 Inf 12.297 <.0001
RVF Local 100 - RVF Global 100 7.226 2.885 Inf 2.505 .06
1 Estimated global bias (ms)


Estimated LVF Global Bias for EHI of +100
LVF_global_bias
28.121


\[ 28.533 - 15.797 = 12.736ms \\ 12.736/200 = 0.064ms / EHI unit \] Each unit change in EHI (-100:100) corresponds to a 0.064ms difference in LVF global bias. This is the slope estimate given by the summary function: 

summary(rt_ehi_model)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ field:level:ehi + field:level + field:ehi + level:ehi +  
##     field + level + ehi + (1 | subject)
##    Data: aah_for_rt_model
## 
## REML criterion at convergence: 913219.1
## 
## Scaled residuals: 
##       Min        1Q    Median        3Q       Max 
## -4.865845 -0.594984 -0.166179  0.370206  7.221165 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subject  (Intercept) 27265.5  165.123 
##  Residual             42379.5  205.863 
## Number of obs: 67505, groups:  subject, 555
## 
## Fixed effects:
##                            Estimate  Std. Error  t value
## (Intercept)             676.7291428   8.0820990 83.73185
## fieldLVF                -20.4882991   2.5020157 -8.18872
## levelLocal               11.5963014   2.5201333  4.60146
## ehi                       0.1317520   0.0966802  1.36276
## fieldLVF:levelLocal      22.1648880   3.5619717  6.22265
## fieldLVF:ehi              0.0566721   0.0299355  1.89314
## levelLocal:ehi           -0.0475090   0.0301604 -1.57521
## fieldLVF:levelLocal:ehi   0.0636820   0.0426037  1.49475
## 
## Correlation of Fixed Effects:
##             (Intr) fldLVF lvlLcl ehi    flLVF:L flLVF: lvlLc:
## fieldLVF    -0.156                                           
## levelLocal  -0.155  0.500                                    
## ehi         -0.458  0.071  0.070                             
## fldLVF:lvlL  0.109 -0.702 -0.707 -0.050                      
## fieldLVF:eh  0.071 -0.456 -0.227 -0.156  0.320               
## levelLocl:h  0.070 -0.227 -0.454 -0.155  0.321   0.500       
## fldLVF:lvL: -0.050  0.321  0.321  0.110 -0.456  -0.703 -0.707

Accuracy

Plots

Statistics

In progress. Model accuracy as a binomial effect of field, level, and EHI (continuous):

acc_ehi_model <- glmer( rt ~ field*level*ehi + (1 | subject), family = "binomial" )